Papers by Tenggan Zhang

2 papers
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database (2022.acl-long)

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Challenge: Existing data resources to support multimodal affective analysis in dialogues are limited in scale and diversity.
Approach: They propose a multimodal multi-scene multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series.
Outcome: The proposed dataset contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances.
ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024.acl-long)

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Challenge: Emotion-focused and strategy-driven chain-of-thought (ESCoT) is a new paradigm for emotional support dialogues.
Approach: They propose an emotional support response generation scheme to improve interpretability . they generate a dataset and develop a model to generate dialogue responses with better interpretability.
Outcome: The proposed scheme can generate dialogue responses with better interpretability.

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